store = {}
store['args']={'batch_size': 64, 'scoring_batch_size': 1000, 'test_batch_size': 1000, 'validation_set_size': 1000, 'early_stopping_patience': 3, 'epochs': 30, 'epoch_samples': 5056, 'num_inference_samples': 20, 'available_sample_k': 10, 'num_iterations': 100, 'no_cuda': False, 'name': 'bald_20_817488', 'seed': 817488, 'log_interval': 10, 'type': 'AcquisitionFunction.bald'}
store['iterations']=[]
store['initial_samples']=[50749, 8681, 23056, 1654, 35076, 50987, 47195, 11321, 4837, 28312, 15024, 39831, 14921, 46218, 37402, 2347, 29223, 37909, 43663, 43970]
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.6327, 'nll': 2.9298627614974975}, 'chosen_samples': [2063, 20172, 40846, 4698, 38111, 8675, 46897, 50783, 41504, 37373], 'chosen_samples_score': ['1.1409715', '1.1424351', '1.1444476', '1.1684121', '1.1775413', '1.1794096', '1.1903985', '1.2083064', '1.2386026', '1.2092707']})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.6861, 'nll': 2.2274397134780886}, 'chosen_samples': [12411, 25341, 47214, 9381, 10261, 30127, 23397, 57736, 42821, 41557], 'chosen_samples_score': ['1.0689182', '1.0717096', '1.0738461', '1.0995867', '1.0830216', '1.146512', '1.1303709', '1.0923984', '1.0794837', '1.1383097']})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7203, 'nll': 1.8586182832717895}, 'chosen_samples': [7842, 14484, 32872, 16220, 47095, 51373, 4914, 19867, 57232, 12733], 'chosen_samples_score': ['1.1013708', '1.1024237', '1.1113963', '1.1136361', '1.1050457', '1.1169547', '1.1338053', '1.2652595', '1.1533668', '1.1413097']})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7676, 'nll': 1.2910946488380433}, 'chosen_samples': [23957, 33035, 16353, 7444, 42255, 12514, 55064, 33399, 8886, 21040], 'chosen_samples_score': ['0.95208585', '0.9583678', '0.9596218', '0.9610028', '0.98097783', '0.9597243', '0.99006283', '1.0255036', '1.0524142', '1.0359454']})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7662, 'nll': 1.277133047580719}, 'chosen_samples': [13165, 46996, 31396, 48098, 24047, 13030, 49537, 19524, 14619, 57882], 'chosen_samples_score': ['0.897202', '0.9285018', '0.914438', '0.98912627', '0.9316333', '1.0749106', '0.9533655', '0.9182991', '0.9114729', '0.9346323']})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7807, 'nll': 1.1046519815921783}, 'chosen_samples': [46562, 33429, 7168, 11708, 27113, 15679, 33401, 10190, 14623, 28226], 'chosen_samples_score': ['0.8349643', '0.8605418', '0.8367318', '0.8650413', '0.8663394', '0.87544966', '0.8806507', '0.8718171', '0.87191474', '0.8687456']})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7851, 'nll': 1.0964295268058777}, 'chosen_samples': [16250, 34678, 35450, 48996, 13768, 45504, 58395, 47914, 1437, 31253], 'chosen_samples_score': ['0.83244026', '0.8339482', '0.8362878', '0.8539663', '0.8535589', '0.8620963', '0.84636045', '0.86136246', '0.89879495', '0.86603355']})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8427, 'nll': 0.9426521062850952}, 'chosen_samples': [28128, 8258, 19569, 49784, 2732, 40450, 57820, 3694, 9984, 44998], 'chosen_samples_score': ['0.97891265', '0.98396415', '0.9900644', '1.0426159', '1.0196999', '1.1134458', '0.9936876', '1.1515331', '0.99795985', '1.0086012']})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8642, 'nll': 0.7799492597579956}, 'chosen_samples': [57632, 38760, 37397, 12196, 22673, 26791, 16778, 45073, 44882, 14139], 'chosen_samples_score': ['0.8337582', '0.84482634', '0.9099081', '0.90324783', '0.83803695', '0.87916917', '0.834906', '0.9333247', '0.86911136', '0.8645878']})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.8955, 'nll': 0.7127739667892456}, 'chosen_samples': [29611, 54896, 18610, 49992, 59401, 38930, 20150, 32918, 12211, 3070], 'chosen_samples_score': ['1.0240853', '1.0734347', '1.0290534', '1.0551455', '1.0458671', '1.0315742', '1.0950178', '1.0443642', '1.0296583', '1.0354896']})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.9093, 'nll': 0.6032822757959366}, 'chosen_samples': [4061, 10210, 38338, 55743, 22915, 49571, 28632, 1812, 1024, 46132], 'chosen_samples_score': ['0.9355007', '0.9450475', '0.9576992', '0.9705467', '1.0482949', '0.9959583', '0.97106725', '1.0110708', '0.99102634', '0.99548703']})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.9065, 'nll': 0.5924564987421036}, 'chosen_samples': [5474, 38577, 47486, 15949, 15723, 40599, 554, 31413, 47737, 12937], 'chosen_samples_score': ['0.94307756', '0.950896', '0.96114254', '0.9642275', '1.0424807', '0.9852812', '0.9710197', '0.97808444', '0.9839807', '0.99061644']})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.9111, 'nll': 0.5736491471529007}, 'chosen_samples': [26034, 12581, 58536, 37489, 42746, 22543, 11038, 1806, 18240, 15855], 'chosen_samples_score': ['0.8934012', '0.8960706', '0.90437824', '0.92503214', '0.9113528', '0.95019895', '0.8967289', '0.9299593', '1.0010488', '0.903894']})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.9129, 'nll': 0.6099583268165588}, 'chosen_samples': [42020, 42397, 21686, 44040, 49515, 2765, 28412, 44342, 15191, 41789], 'chosen_samples_score': ['0.98906213', '1.0350903', '1.0805583', '1.0622745', '1.0094739', '1.0512673', '1.0403748', '1.0444826', '1.098099', '1.0598903']})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.9101, 'nll': 0.6125839859247207}, 'chosen_samples': [49674, 23962, 3719, 13096, 38298, 40702, 15763, 50317, 37048, 7886], 'chosen_samples_score': ['0.987221', '1.000503', '0.99472994', '1.0019906', '1.0064768', '1.0461628', '1.1875435', '1.0531375', '1.0737231', '1.0084457']})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.9216, 'nll': 0.5500324457883835}, 'chosen_samples': [28512, 15781, 20050, 30047, 47951, 9687, 9625, 47741, 34328, 40678], 'chosen_samples_score': ['0.9852379', '0.99287885', '0.9984541', '1.0099709', '1.107167', '1.0106022', '1.0869849', '1.0397986', '1.0259912', '1.0450034']})
store['iterations'].append({'num_epochs': 10, 'test_metrics': {'accuracy': 0.9393, 'nll': 0.48945689350366595}, 'chosen_samples': [13337, 5315, 31512, 24391, 23927, 10412, 43226, 19396, 59314, 15252], 'chosen_samples_score': ['1.0387503', '1.0397761', '1.0459025', '1.0556719', '1.052801', '1.0566182', '1.0695059', '1.0927032', '1.1169084', '1.1819823']})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.9278, 'nll': 0.5148372530937195}, 'chosen_samples': [30770, 5170, 59747, 42703, 17518, 26412, 51960, 2192, 32776, 39947], 'chosen_samples_score': ['0.94012475', '0.9451349', '0.95250547', '1.0166898', '0.9886102', '0.9918823', '0.94811124', '0.96733785', '0.9886238', '0.9787934']})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.9264, 'nll': 0.4802160769701004}, 'chosen_samples': [547, 20170, 52140, 3742, 12934, 181, 16748, 36417, 36818, 29180], 'chosen_samples_score': ['0.864316', '0.8815706', '0.8837864', '0.8863781', '1.0582025', '0.9275385', '0.88386923', '0.9098478', '0.9098023', '0.950917']})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.9391, 'nll': 0.4349870026111603}, 'chosen_samples': [12037, 51987, 48057, 27429, 36704, 12986, 16637, 11565, 54994, 44480], 'chosen_samples_score': ['0.90371424', '0.90416163', '0.90468585', '0.9528558', '0.93585175', '0.9383511', '0.9068434', '0.9289469', '0.9070948', '1.0389683']})
store['iterations'].append({'num_epochs': 9, 'test_metrics': {'accuracy': 0.9457, 'nll': 0.40258354842662813}, 'chosen_samples': [13942, 57714, 46610, 8887, 13538, 26444, 32427, 22470, 11292, 29476], 'chosen_samples_score': ['0.98202807', '0.9935799', '0.99798584', '1.000979', '1.0040122', '1.0117556', '1.0499003', '1.0968568', '1.1586359', '1.1053851']})
store['iterations'].append({'num_epochs': 10, 'test_metrics': {'accuracy': 0.9483, 'nll': 0.4169569104909897}, 'chosen_samples': [602, 22537, 18487, 33388, 42428, 5632, 20709, 47076, 6466, 50562], 'chosen_samples_score': ['1.0266948', '1.0268545', '1.0500069', '1.1148208', '1.0375001', '1.0682359', '1.073286', '1.0660256', '1.0416722', '1.0770283']})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.9525, 'nll': 0.35774744153022764}, 'chosen_samples': [23350, 25508, 31090, 22481, 53872, 53736, 37469, 23112, 39561, 43609], 'chosen_samples_score': ['0.89203644', '0.8965781', '0.915211', '0.90102726', '0.9170521', '0.918806', '0.9189646', '0.9208459', '0.9205868', '0.9353745']})
store['iterations'].append({'num_epochs': 10, 'test_metrics': {'accuracy': 0.9559, 'nll': 0.34748354405164716}, 'chosen_samples': [38275, 49525, 24883, 43042, 48706, 3367, 43575, 9180, 30147, 224], 'chosen_samples_score': ['1.0038884', '1.0092297', '1.0107944', '1.0152538', '1.0194232', '1.0269313', '1.0942523', '1.1041229', '1.0660486', '1.0599201']})
store['iterations'].append({'num_epochs': 9, 'test_metrics': {'accuracy': 0.9525, 'nll': 0.34254381954669955}, 'chosen_samples': [49998, 6428, 7852, 17045, 4459, 19495, 48649, 59309, 1075, 26733], 'chosen_samples_score': ['0.9095946', '0.9250872', '0.9260582', '0.94114435', '0.9482718', '0.9780475', '0.95543325', '0.9825699', '0.9881705', '1.0460856']})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.954, 'nll': 0.36643289774656296}, 'chosen_samples': [32513, 17712, 26358, 11950, 59335, 45026, 36126, 38408, 36810, 40158], 'chosen_samples_score': ['0.9445169', '0.94971305', '0.9524374', '0.95793134', '0.97596306', '0.98143977', '0.9661145', '0.9938831', '0.9826281', '1.0467933']})
store['iterations'].append({'num_epochs': 11, 'test_metrics': {'accuracy': 0.9547, 'nll': 0.3587367281317711}, 'chosen_samples': [27164, 28930, 44414, 41578, 25332, 42384, 41426, 24221, 25246, 54814], 'chosen_samples_score': ['0.9914037', '0.9944105', '1.0082691', '1.010462', '1.1217806', '1.0398183', '1.0118124', '1.0054734', '1.047511', '1.0927019']})
store['iterations'].append({'num_epochs': 12, 'test_metrics': {'accuracy': 0.9606, 'nll': 0.34754929393529893}, 'chosen_samples': [16756, 7368, 52582, 32880, 6474, 49910, 2302, 44123, 33162, 51004], 'chosen_samples_score': ['1.0550711', '1.0580521', '1.0667541', '1.0652726', '1.0671777', '1.0682993', '1.0725281', '1.0770423', '1.1966954', '1.0907089']})
store['iterations'].append({'num_epochs': 9, 'test_metrics': {'accuracy': 0.9617, 'nll': 0.33134532123804095}, 'chosen_samples': [53324, 8704, 34847, 34520, 24662, 29153, 670, 9633, 45853, 39355], 'chosen_samples_score': ['0.91999096', '0.93345004', '0.93762255', '0.9403554', '0.9413453', '0.96644115', '0.9728282', '1.0211825', '1.0375904', '1.0672163']})
store['iterations'].append({'num_epochs': 10, 'test_metrics': {'accuracy': 0.9597, 'nll': 0.3266082599759102}, 'chosen_samples': [20820, 1674, 18324, 57665, 49656, 45056, 34058, 38698, 6755, 4955], 'chosen_samples_score': ['0.93664676', '0.93698967', '0.9603386', '0.9801476', '0.950298', '0.95380753', '0.9390792', '0.99815327', '1.0066085', '1.0569222']})
store['iterations'].append({'num_epochs': 10, 'test_metrics': {'accuracy': 0.9637, 'nll': 0.305367811024189}, 'chosen_samples': [17190, 50431, 29320, 19324, 31954, 59430, 16676, 29440, 35401, 36744], 'chosen_samples_score': ['0.9009977', '0.9148856', '0.9113642', '0.9051284', '0.9148846', '0.9127648', '0.9291756', '0.95240074', '0.9817623', '0.9377663']})
store['iterations'].append({'num_epochs': 11, 'test_metrics': {'accuracy': 0.9623, 'nll': 0.32361883372068406}, 'chosen_samples': [49497, 53379, 48929, 37078, 12950, 51436, 29530, 5298, 49890, 2202], 'chosen_samples_score': ['0.94008905', '0.94344884', '0.9524628', '0.9664503', '0.958684', '0.9819544', '0.98654366', '1.0070794', '1.0451427', '1.1054897']})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.9592, 'nll': 0.3490258648991585}, 'chosen_samples': [37596, 57507, 39925, 6418, 12070, 34616, 42078, 9118, 42642, 52169], 'chosen_samples_score': ['0.79993165', '0.80295074', '0.8085864', '0.82659465', '0.91067415', '0.9406825', '0.82233936', '0.90219635', '0.8098284', '0.85961545']})
store['iterations'].append({'num_epochs': 10, 'test_metrics': {'accuracy': 0.9605, 'nll': 0.3208034113049507}, 'chosen_samples': [22169, 262, 34665, 13969, 14754, 42734, 36337, 41307, 22083, 2845], 'chosen_samples_score': ['0.9326135', '0.9435911', '0.9544281', '1.0157914', '0.9559799', '1.0300214', '0.98320544', '0.9571918', '1.0830116', '0.98655885']})
store['iterations'].append({'num_epochs': 9, 'test_metrics': {'accuracy': 0.967, 'nll': 0.31571377962827685}, 'chosen_samples': [55282, 18739, 33642, 55739, 11482, 38050, 52694, 16836, 14385, 21436], 'chosen_samples_score': ['0.9040965', '0.9177974', '0.941311', '0.9043586', '0.9770166', '0.96472764', '0.99623895', '1.1845145', '0.907302', '0.9509994']})
store['iterations'].append({'num_epochs': 12, 'test_metrics': {'accuracy': 0.9676, 'nll': 0.2690549075603485}, 'chosen_samples': [34396, 53656, 41171, 39429, 46815, 12305, 5679, 25055, 56082, 55028], 'chosen_samples_score': ['0.9417004', '0.94194275', '0.94194144', '0.96853477', '0.9732412', '0.9754821', '0.99161404', '1.0004207', '1.0180327', '1.0426409']})
store['iterations'].append({'num_epochs': 12, 'test_metrics': {'accuracy': 0.9669, 'nll': 0.27403427064418795}, 'chosen_samples': [28860, 148, 15386, 18130, 12078, 8228, 20870, 54885, 28246, 59427], 'chosen_samples_score': ['0.96240705', '0.9628838', '0.96838874', '0.9715547', '0.9715656', '1.0032454', '1.040057', '1.003787', '1.0007455', '1.0004681']})
store['iterations'].append({'num_epochs': 9, 'test_metrics': {'accuracy': 0.9601, 'nll': 0.3190245747566223}, 'chosen_samples': [29360, 9472, 15913, 27458, 23629, 56190, 8300, 13259, 21700, 30041], 'chosen_samples_score': ['0.8689205', '0.8740684', '0.8800018', '0.8887143', '0.891888', '1.0211437', '0.96103466', '0.9042486', '0.9169738', '0.9102675']})
store['iterations'].append({'num_epochs': 13, 'test_metrics': {'accuracy': 0.9651, 'nll': 0.28402457684278487}, 'chosen_samples': [59653, 14246, 26737, 635, 7000, 40066, 7182, 18003, 9433, 497], 'chosen_samples_score': ['0.94426817', '0.94627345', '0.9471843', '0.9476128', '0.9643095', '0.9679898', '1.001833', '1.0222028', '0.9988851', '0.9791615']})
store['iterations'].append({'num_epochs': 9, 'test_metrics': {'accuracy': 0.9645, 'nll': 0.3084383472800255}, 'chosen_samples': [24426, 12792, 47475, 32747, 54966, 37552, 10028, 32323, 43034, 39778], 'chosen_samples_score': ['0.81412786', '0.8180617', '0.83138275', '0.8243003', '0.8369406', '0.8893362', '0.8641324', '0.9306438', '0.84622586', '0.8534968']})
store['iterations'].append({'num_epochs': 10, 'test_metrics': {'accuracy': 0.9646, 'nll': 0.31651858389377596}, 'chosen_samples': [13912, 52086, 1454, 53999, 18598, 34698, 31046, 5684, 45801, 6347], 'chosen_samples_score': ['0.9205241', '0.92268336', '0.9389594', '0.93979144', '0.94447404', '0.9472721', '0.97061455', '0.94979054', '0.9715423', '1.0219815']})
store['iterations'].append({'num_epochs': 9, 'test_metrics': {'accuracy': 0.9689, 'nll': 0.29053422808647156}, 'chosen_samples': [2980, 3692, 21880, 9431, 41084, 16406, 51764, 22531, 23140, 13779], 'chosen_samples_score': ['0.8328616', '0.8769056', '0.85107017', '0.8372088', '0.8508794', '0.897667', '0.9589437', '1.0222391', '1.006256', '0.9087966']})
store['iterations'].append({'num_epochs': 12, 'test_metrics': {'accuracy': 0.9698, 'nll': 0.27398685216903684}, 'chosen_samples': [49573, 29361, 26850, 52975, 41573, 13878, 21601, 9081, 31313, 45944], 'chosen_samples_score': ['0.89440715', '0.9141164', '0.92159945', '0.9414976', '0.9458854', '1.0134478', '0.9826456', '0.9661006', '1.0235171', '1.0572472']})
store['iterations'].append({'num_epochs': 13, 'test_metrics': {'accuracy': 0.9723, 'nll': 0.2565080001950264}, 'chosen_samples': [20641, 41283, 56480, 17603, 43618, 53844, 41802, 2292, 517, 16795], 'chosen_samples_score': ['0.8924108', '0.89964277', '0.894569', '0.9092092', '0.95357156', '1.1164825', '0.91430503', '1.0455556', '0.9131758', '0.9262796']})
store['iterations'].append({'num_epochs': 15, 'test_metrics': {'accuracy': 0.973, 'nll': 0.2673316180706024}, 'chosen_samples': [43788, 24589, 26258, 14062, 1518, 6231, 9448, 39480, 40654, 38195], 'chosen_samples_score': ['0.96803564', '0.970214', '0.97212523', '0.97468156', '0.97584176', '0.9871322', '0.9989742', '1.0215056', '1.0227013', '1.026154']})
store['iterations'].append({'num_epochs': 13, 'test_metrics': {'accuracy': 0.9763, 'nll': 0.2415308579802513}, 'chosen_samples': [14722, 12985, 47445, 43532, 17772, 58560, 59701, 13078, 17382, 59286], 'chosen_samples_score': ['0.9461162', '0.952578', '0.9685357', '0.97598934', '1.0287569', '1.0624757', '1.0671389', '1.0436262', '0.98138535', '1.0226403']})
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